class UpperCAmelCase		:


				'''simple docstring'''






				def __init__(		self						,      lowercase   ):



						"""simple docstring"""





						A_						:							Dict      = val
						A_						:							Any      = None
						A_						:							Dict      = None







				def     lowerCAmelCase_  (		self						,      lowercase   ):



						"""simple docstring"""





						if self.val:
								if val < self.val:
										if self.left is None:
												A_						:							Optional[Any]      = Node(lowercase   )
										else:
												self.left.insert(lowercase   )
								elif val > self.val:
										if self.right is None:
												A_						:							str      = Node(lowercase   )
										else:
												self.right.insert(lowercase   )
						else:
								A_						:							List[Any]      = val




def 							UpperCamelCase		(			__lowercase		:					Optional[Any]		,__lowercase		:					str      ):
		'''simple docstring'''




		if root:
				inorder(root.left		,__lowercase      )
				res.append(root.val      )
				inorder(root.right		,__lowercase      )




def 							UpperCamelCase		(			__lowercase		:					List[str]      ):
		'''simple docstring'''




		if len(__lowercase      ) == 0:
				return arr
		A_						:							List[Any]      = Node(arr[0]      )
		for i in range(1		,len(__lowercase      )      ):
				root.insert(arr[i]      )
		# Traverse BST in order.
		A_						:							int      = []
		inorder(__lowercase		,__lowercase      )
		return res


if __name__ == "__main__":
	print(tree_sort([10, 1, 3, 2, 9, 14, 13]))







import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


_UpperCAmelCase          =						{
    """n_samples""": 64,
    """horizon""": 32,
    """num_inference_steps""": 20,
    """n_guide_steps""": 2,  # can set to 0 for faster sampling, does not use value network
    """scale_grad_by_std""": True,
    """scale""": 0.1,
    """eta""": 0.0,
    """t_grad_cutoff""": 2,
    """device""": """cpu""",
}


if __name__ == "__main__":
	_UpperCAmelCase          =						"""hopper-medium-v2"""
	_UpperCAmelCase          =						gym.make(env_name)

	_UpperCAmelCase          =						ValueGuidedRLPipeline.from_pretrained(
	    """bglick13/hopper-medium-v2-value-function-hor32""",
	    env=env,
	)

	env.seed(0)
	_UpperCAmelCase          =						env.reset()
	_UpperCAmelCase          =						0
	_UpperCAmelCase          =						0
	_UpperCAmelCase          =						1000
	_UpperCAmelCase          =						[obs.copy()]
	try:
		for t in tqdm.tqdm(range(T)):
			# call the policy
			_UpperCAmelCase          =						pipeline(obs, planning_horizon=32)

			# execute action in environment
			_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase          =						env.step(denorm_actions)
			_UpperCAmelCase          =						env.get_normalized_score(total_reward)

			# update return
			total_reward += reward
			total_score += score
			print(
			    F"""Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:"""
			    F""" {total_score}"""
			)

			# save observations for rendering
			rollout.append(next_observation.copy())

			_UpperCAmelCase          =						next_observation
	except KeyboardInterrupt:
		pass

	print(F"""Total reward: {total_reward}""")






